There are a number of well documented challenges relating to resolving entities mentioned in multiple documents and collapsing the documents based solely on entity, attributes and relationships extracted by most text analytics pipelines (for example Stanford Parser). Facts extracted from the documents are typically sparse (i.e., very few facts) and fuzzy (e.g., natural language can be ambiguous), or difficult to associate to a specific entity in a document. The more entities there are in the document, the more complicated it is to associate the sparse facts to a correct entity.